Surgery

Diagnostic accuracy of Impedance Spectroscopy versus Digital Rectal Examination for Obstetric Anal Sphincter Injuries: a postpartum post-hoc analysis

Accurate diagnosis of obstetric anal sphincter injuries (OASIs) is critical for timely repair and prevention of long-term morbidity, yet digital rectal examination (DRE) remains insufficiently sensitive. In this post-hoc analysis of a prospective multicentre diagnostic study (NCT04903977), we compared the diagnostic performance of DRE and machine learning-assisted impedance spectroscopy with three...

Optical Microscopy Predictions of Focal Recurrence in Glioblastoma

A hallmark of glioblastoma (GBM) is disease recurrence, which occurs in all patients despite tumor resection, radiation, and chemotherapy. A critical challenge in glioblastoma treatment is the management of recurrent disease, for which there is no standard of care. Predicting the location of glioblastoma recurrence may improve the efficiency of advanced-stage therapies. Here, we present an artific...

Temperature dominates dengue transmission in Thailand: Machine learning reveals critical thresholds and COVID-19 disruption

Dengue fever remains a critical public health challenge in Thailand, with transmission dynamics driven by complex interactions between environmental a...

Prediction of recurrence and functional status in young ischemic stroke patients: Comparison of machine learning and traditional statistical methods

Ischemic stroke in young adults is a significant social and economic burden. Machine learning (ML) techniques can potentially predict the outcomes of ...

Predictive modeling of hematoma expansion from non-contrast computed tomography in spontaneous intracerebral hemorrhage patients

Hematoma expansion is a consistent predictor of poor neurological outcome and mortality after spontaneous intracerebral hemorrhage (ICH). An incomplet...

ECG classification with convolutional neural networks demonstrates resilience to sex-imbalances in data

Many ECG-AI models have been developed to predict a wide range of cardiovascular outcomes. The underrepresentation of women in cardiovascular disease ...

CardiacGPT™: A Real-Time AI Assistant for Intraoperative Guidance and Postoperative Decision Support in Cardiac Surgery

Cardiac surgery is one of the most complex and high-stakes areas of medicine, where intraoperative decisions must be made within seconds and incomplet...

A Virtual Patients Ensemble Approach for Predicting Surgical Complications

AI has shown promise in predicting surgical complications, but most existing models estimate overall risk levels rather than identifying the specific ...

Ensembles of temporal models and forward-backward smoothing for surgical phase recognition

Surgical phase recognition from endoscopic video could enable numerous context-aware technologies that impact efficiency and performance of surgeons a...

Deep learning-based prediction of cardiopulmonary disease in retinal images of premature infants

Bronchopulmonary dysplasia (BPD) and pulmonary hypertension (PH) are leading causes of morbidity and mortality in premature infants. To determine whet...

Diagnostic Performance of Self-Supervised Foundation Models for Intraoperative Quantification of Hepatic Macrovesicular Steatosis

Accurate intraoperative assessment of macrovesicular steatosis in donor liver biopsies is critical for transplantation decisions but is often limited ...

BeatAI: BiomEtrics for Atrial Arrhythmia Tracking Using Artificial Intelligence

Postoperative atrial fibrillation (POAF) affects 20 to 50% of patients undergoing cardiac surgery and is associated with longer hospital stays and adv...

Appendix300: A multi-institutional laparoscopic appendectomy video dataset for computational modeling tasks

The limited availability of diverse and representative training data poses a critical barrier to the development of clinically relevant computational ...

Personalized Hemodynamic Management Using Reinforcement Learning to Prevent Persistent Acute Kidney Injury After Cardiac Surgery

Acute kidney injury (AKI) affects one-third of patients after cardiac surgery and increases morbidity and mortality. AKI lasting over 48 hours, known ...

Long-Term Carotid Plaque Progression and the Role of Intraplaque Hemorrhage: A Deep Learning-Based Analysis of Longitudinal Vessel Wall Imaging

Carotid atherosclerosis is a major contributor in the etiology of ischemic stroke. Although intraplaque hemorrhage (IPH) is known to increase stroke r...

SAHDAI-XAI Subarachnoid Hemorrhage Detection Artificial Intelligence- eXplainable AI: Testing explainability in SAH Imaging Data and AI Modeling

Subarachnoid hemorrhage (SAH) is a life-threatening and crucial neurological emergency. SAHDAI-XAI (Subarachnoid Hemorrhage Detection Artificial Intel...

Dietary Macronutrient Intake and the Gut Microbiome in Adults Undergoing Bariatric Surgery for Obesity

Limited information linking dietary intake to gut metagenomic data in bariatric surgery patients is available. We examined whether there were correlat...

Classifying the severity of diabetic macular oedema from optical coherence tomography scans using deep learning: a feasibility study

Diabetic macular oedema (DME) is a vision-threatening complication of diabetes mellitus. It is reliably detected using optical coherence tomography (O...

Explainable Deep Learning for Lesion-Level Detection of Diabetic Retinopathy: A Segmentation Approach Using Fundus Images Graded as Mild-to-Moderate Nonproliferative Diabetic Retinopathy

Deep learning has shown promise in diabetic retinopathy screening using fundus images. However, many existing models operate as “black boxes,” providi...

From Concept to Code: AI- Powered CODE-ICH Transforming Acute Neurocritical Response for Hemorrhagic Strokes

Intracerebral hemorrhage (ICH) is among the most devastating forms of stroke, characterized by high early mortality and limited time-sensitive treatme...